About this trial
This project aims to establish the feasibility of Bayesian optimization for tuning deep brain stimulation (DBS) to treat gait symptoms in Parkinson's disease (PD) patients. Our primary question is: Can Bayesian optimization of DBS achieve reproducible results within a feasible number of gait measurements? PD patients will be enrolled who have DBS of the subthalamic nucleus (STN) or globus pallidus (GP) in whom at least 3 months have passed since activation of their neurostimulators, for stabilization of clinical stimulator settings. We will apply Bayesian optimization to derive DBS settings which maximally lengthen step length relative to the OFF DBS state.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Diagnosis of Parkinson's Disease
DBS in STN or GP (bilateral or unilateral)
At least 3 months after lead implantation
Disqualifiers
Inability to walk in the off-med, off-stimulation condition (even with safety harness)
Gait impaired significantly by a condition other than PD, as determined by the PI
Breaks or shorts in active contacts
IPG battery nearing end of life (in patients with primary-cell IPGs)
Trial design
Single group
Treatments tested in this trial
Deep Brain Stimulation
Other interventionDBS within FDA-approved limits and labeling for symptoms of PD
Treatment groups
Trial outcomes
Primary outcomes
Gait Test
Subjects walk for one minute each on a treadmill set to match their individual overground walking speed. A forceplate measures the center of pressure trajectory from which integral software detects time and location of heel-strike and toe-off events. These events, and the treadmill speed, are used to compute step lengths.
Sponsors and contacts
Click on the lead sponsor to view all of their trials.